Brief bibliography of interestingness measure, bayesian belief network and causal inference papers
Upcoming SlideShare
Loading in...5
×
 

Brief bibliography of interestingness measure, bayesian belief network and causal inference papers

on

  • 621 views

 

Statistics

Views

Total Views
621
Views on SlideShare
621
Embed Views
0

Actions

Likes
0
Downloads
2
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Brief bibliography of interestingness measure, bayesian belief network and causal inference papers Brief bibliography of interestingness measure, bayesian belief network and causal inference papers Document Transcript

  • A Brief Bibliography of Interestingness Measure, Bayesian Belief Network and Causal Inference Papers by Adnan Masood Doctoral Student http://scis.nova.edu/~adnan Graduate School of Computer and Information Sciences Nova Southeastern University 2012
  • ReferencesAbramson, B., & Ng, K. (1993). Toward an art and science of knowledge engineering: A case for belief networks. IEEE Transactions on Knowledge and Data Engineering, 5(4), 705-712.Aggarwal, C., & Yu, P. (2001). Outlier detection for high dimensional data. ACM Sigmod Record, 30(2), 37-46.Agyemang, M., Barker, K., & Alhajj, R. (2006). A comprehensive survey of numeric and symbolic outlier mining techniques. Intelligent Data Analysis, 10(6), 521–538.Aljandal, W. (2009). Itemset size-sensitive interestingness measures for association rule mining and link prediction. UMI Dissertation Publishing - ProQuest.Andersen, S., Olesen, K., Jensen, F., & Jensen, F. (1989). Hugin shell for building bayesian belief universes for expert systems. In Proceedings of the 11th international joint conference on artificial intelligence (Vol. 2, pp. 1080–1085). Morgan Kaufmann Publishers Inc.Babbar, S., & Chawla, S. (2010). On bayesian network and outlier detection. Management of Data, 125.Bednarski, M., Cholewa, W., & Frid, W. (2004). Identification of sensitivities in bayesian networks. Engi- neering Applications of Artificial Intelligence, 17(4), 327–335.Bell, G., Hey, T., & Szalay, A. (2009). Beyond the data deluge. Science, 323(5919), 1297–1298.Bouckaert, R. (2007). Bayesian network classifiers in weka for version 3-5-6. The University of Waikato.Box, G., & Tiao, G. (1968). A bayesian approach to some outlier problems. Biometrika, 55(1), 119.Brailovsky, V. (1996). An approach to outlier detection based on bayesian probabilistic model. In Proceedings of the 13th international conference on pattern recognition, 1996. (Vol. 2, pp. 70–74).Buntine, W. (1996). A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8(2), 195-210.Cannady, J. (1998). Artificial neural networks for misuse detection. In Proceedings of the 1998 national information systems security conference (nissc’98) (p. 443-456). National Institute of Standards and Technology.Carvalho, D., Freitas, A., & Ebecken, N. (2005). Evaluating the correlation between objective rule in- terestingness measures and real human interest. Knowledge Discovery in Databases: PKDD 2005, 453-461.Chan, H. (2006). Sensitivity analysis of probabilistic graphical models (Doctoral dissertation, University of California). UMI Dissertation Publishing - ProQuest.Chan, H. (2009). Sensitivity analysis of bayesian networks and its application for service engineering. In Soft computing and pattern recognition, 2009. socpar’09. international conference of (pp. 551–556).Chan, H., & Darwiche, A. (2002). Reasoning about bayesian network classifiers. In Proceedings of the nineteenth conference on uncertainty in artificial intelligence (pp. 107–115).Chan, H., & Darwiche, A. (2004). Sensitivity analysis in bayesian networks: From single to multiple parameters. In Proceedings of the 20th conference on uncertainty in artificial intelligence (pp. 67– 75).Chan, H., & Darwiche, A. (2005). A distance measure for bounding probabilistic belief change. International Journal of Approximate Reasoning, 38(2), 149–174.Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.Charniak, E. (1991). Bayesian networks without tears. AI magazine, 12(4), 50.Chen, R. C., Chen, T. S., & Lin, C. C. (2006). A new binary support vector system for increasing detection rate of credit card fraud. International Journal of Pattern Recognition and Artificial Intelligence, 20(2), 227–240.Cheng, J., & Greiner, R. (1999). Comparing bayesian network classifiers. In Proceedings of the fifteenth conference on uncertainty in artificial intelligence (pp. 101–108).Cheng, J., & Greiner, R. (2001). Learning bayesian belief network classifiers: Algorithms and system. Advances in Artificial Intelligence, 141-151.
  • 3Choi, A., & Darwiche, A. (2011). Relax, compensate and then recover. New Frontiers in Artificial Intelli- gence, 167–180.Cooper, G. (1988). A method for using belief networks as influence diagrams. In Workshop on uncertainty in artificial intelligence (pp. 55–63).Cooper, G. (1990). Probabilistic inference using belief networks is np-hard. Artificial Intelligence, 42(1), 393-405.Cooper, G., & Herskovits, E. (1992). A bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4), 309-347.Coupe, V., & Van Der Gaag, L. (1998). Practicable sensitivity analysis of bayesian belief networks. UU- CS(1998-10).Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel- based learning methods. Cambridge university press.De Finetti, B. (1961). The bayesian approach to the rejection of outliers. In Proceedings of the fourth berkeley symposium on mathematical statistics and probability, volume 1: Contributions to the theory of statistics (Vol. 1, pp. 199–210). Univ of California Press.Dittmer, S., & Jensen, F. (1997). Tools for explanation in bayesian networks with application to an agricultural problem. In Proceedings of the first european conference for information technology in agriculture (pp. 15–18).Druzdel, M., & Van Der Gaag, L. (2000). Building probabilistic networks:" where do the numbers come from?". IEEE Transactions on knowledge and data engineering, 12(4), 481–486.Druzdzel, M. (1996). Qualitiative verbal explanations in bayesian belief networks. AISB QUARTERLY, 43–54.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.Ferreira, P., Alves, R., Belo, O., & Cortes, L. (2006). Establishing fraud detection patterns based on signa- tures. Advances in Data Mining, 526–538.Fitelson, B., & Hitchcock, C. (2010). Probabilistic measures of causal strength. Causality in the Sciences.Francisci, D., & Collard, M. (2003). Multi-criteria evaluation of interesting dependencies according to a data mining approach. In In congress on evolutionary computation (Vol. 3, p. 1568-1574). IEEE Press.Frank, A., & Asuncion, A. (2010). UCI machine learning repository. Available from http://archive.ics.uci.edu/mlFriedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine learning, 29(2), 131–163.Garriga, J. (2009). The right will for seeing and believing. UMI Proquest.Garriga, J. (2011). Groundwork for a new approach to knowledge discovery.Geng, L., & Hamilton, H. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR), 38(3), 9.Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009, November). The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1), 10–18. Available from http://doi.acm.org/10.1145/1656274.1656278Han, J., & Kamber, M. (2006). Data mining: concepts and techniques. Morgan Kaufmann.Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., & Toivonen, H. (1996). Knowledge discov- ery from telecommunication network alarm databases. In Proceedings of the twelfth international conference on data engineering, 1996. (pp. 115–122).Hawkins, D. M. (1980). Identification of outliers. Chapman & Hall.Heckerman, D. (2008). A tutorial on learning with bayesian networks. Innovations in Bayesian Networks, 33-82.Henrion, M., & Cooley, D. R. (1987). An experimental comparison of knowledge engineer- ing for expert systems and for decision analysis. In Proceedings of the sixth national con- ference on artificial intelligence - volume 2 (pp. 471–476). AAAI Press. Available from http://dl.acm.org/citation.cfm?id=1863766.1863781
  • 4Hilderman, R., & Hamilton, H. (2001). Knowledge discovery and measures of interest. Springer Netherlands.Hornik, K., Leisch, F., & Zeileis, A. (2003, March). Learning bayesian networks with r. In K. Hornik, F. Leisch, & A. Zeileis (Eds.), Proceedings of the 3rd international workshop on distributed statistical computing (dsc 2003) (p. 2). Vienna, Austria. Available from http://www.r-project.org/conferences/DSC-2003/Proceedings/BottcherDethlefsen.pdfHoward, R., & Matheson, J. (2005). Influence diagrams. Decision Analysis, 2(3), 127–143.Huang, C., Chen, M., & Wang, C. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856.Huelsenbeck, J., & Ronquist, F. (2001). Mrbayes: Bayesian inference of phylogenetic trees. Bioinformatics, 17(8), 754-755.Imberman, S., Domanski, B., & Thompson, H. (2001). Boolean analyzer-an algorithm that uses a probabilis- tic interestingness measure to find dependency/association rules in a head trauma data.Jaroszewicz, S., & Scheffer, T. (2005). Fast discovery of unexpected patterns in data, relative to a bayesian network. In Proceedings of the eleventh acm sigkdd international conference on knowledge discovery in data mining (pp. 118–127).Jaroszewicz, S., Scheffer, T., & Simovici, D. (2009). Scalable pattern mining with bayesian networks as background knowledge. Data Mining and Knowledge Discovery, 18(1), 56–100.Jaroszewicz, S., & Simovici, D. (2004). Interestingness of frequent itemsets using bayesian networks as background knowledge. In Proceedings of the tenth acm sigkdd international conference on knowledge discovery and data mining (pp. 178–186).Jensen, F. (1997). Bayesian networks and influence diagrams. Risk Management Strategies in Agriculture, 199–213.Joshi, M. V., Agarwal, R. C., & Kumar, V. (2001). Mining needle in a haystack: classifying rare classes via two-phase rule induction. In Proceedings of the 2001 acm sigmod international con- ference on management of data (pp. 91–102). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/375663.375673Kaur, H. (2005). Actionable rules: issues and new directions. In Proceedings of world academy of science, engineering and technology (pp. 61–64). World Academy of Science, Engineering and Technology.Khairuddin, M., Zhang, P., & Rao, A. (2008). Risk mitigation strategies for the prepaid card issuer in aus- tralia. Security Research Centre, School of Computer and Security Science, Edith Cowan University, Perth, Western Australia.Khashman, A. (2010, September). Neural networks for credit risk evaluation: Investigation of differ- ent neural models and learning schemes. Expert Syst. Appl., 37(9), 6233–6239. Available from http://dx.doi.org/10.1016/j.eswa.2010.02.101Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.Knorr, E. M., & Ng, R. T. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24rd international conference on very large data bases (pp. 392–403). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Available from http://dl.acm.org/citation.cfm?id=645924.671334Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT press.Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence, second edition (2nd ed.). Boca Raton, FL, USA: CRC Press, Inc.Koski, T., & Noble, J. (2009). Bayesian networks: An introduction (1st ed.). Wiley Publishing.Krieg, M. (2001). A tutorial on bayesian belief networks.Lacave, C., & Díez, F. (2002). A review of explanation methods for bayesian networks. The Knowledge Engineering Review, 17(2), 107–127.Lallich, S., Vaillant, B., & Lenca, P. (2005). Parametrised measures for the evaluation of as- sociation rule interestingness. In Proceedings of the 6th international symposium on ap-
  • 5 plied stochastic models and data analysis (asmda 2005) (pp. 220–229). Available from http://asmda2005.enst-bretagne.fr/IMG/pdf/proceedings/220.pdfLaskey, K. (1995). Sensitivity analysis for probability assessments in bayesian networks. IEEE Transactions on Systems, Man and Cybernetics, 25(6), 901–909.Lawrence, N. (2000). Variational inference in probabilistic models. Unpublished doctoral dissertation, University of Cambridge, UK.Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V., & Srivastava, J. (2008). Data mining for anomaly detection. In Tutorial at the european conference on principles and practice of knowledge discovery in databases, antwerp, belgium, september (Vol. 19).Lazarevic, A., & Kumar, V. (2005). Feature bagging for outlier detection. In Proceedings of the eleventh acm sigkdd international conference on knowledge discovery in data mining (pp. 157–166). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/1081870.1081891Loh, W.-Y. (2008). Classification and regression tree methods. In Encyclopedia of statistics in quality and reliability. John Wiley & Sons, Ltd. Available from http://dx.doi.org/10.1002/9780470061572.eqr492Lyons, L. (2007, November). A particle physicist’s perspective on astrostatistics. In G. J. Babu & E. D. Feigel- son (Eds.), Statistical challenges in modern astronomy iv (Vol. 371, p. 361).Malhas, R., & Aghbari, Z. (2009). Interestingness filtering engine: Mining bayesian networks for interesting patterns. Expert Systems with Applications, 36(3), 5137–5145.Malhas, R., & Al Aghbari, Z. (2006). An efficient bayesian network approach for discovering interesting patterns. Data Mining VII: Data, Text, and Web Mining and Their Business Applications, 103.Malhas, R., & Al Aghbari, Z. (2007). Fast discovery of interesting patterns based on bayesian network background knowledge. University of Sharjah Journal of Pure & Applied Sciences, 4(3), 29–47.Malhas, R., & Al Aghbari, Z. (2008). Using sensitivity of a bayesian network to discover interest- ing patterns. In Proceedings of the 2008 ieee/acs international conference on computer systems and applications (pp. 196–205). Washington, DC, USA: IEEE Computer Society. Available from http://dx.doi.org/10.1109/AICCSA.2008.4493535Meek, C. (1995). Causal inference and causal explanation with background knowledge. In Uncertainty in artificial intelligence (Vol. 11, pp. 403–410). Morgan Kaufmann Publishers, Inc., San Mateo, CA.MejiaÂa-Lavalle, M., & Sanchez Vivar, A. (2009). Outlier detection with explanation facility. Machine Learning and Data Mining in Pattern Recognition, 454-464.Mejia-Lavalle, M. (2010). Outlier detection with innovative explanation facility over a very large financial database. In Electronics, robotics and automotive mechanics conference (cerma), 2010 (pp. 23–27).Mejia-Lavalle, M., M.a-Lavalle, Obregon, R., & Vivar, A. (2009). Outlier detection with a hybrid artificial intelligence method. MICAI 2009: Advances in Artificial Intelligence, 590-599.Minka, T., Winn, J., Guiver, J., & Kannan, A. (2009). Infer .net 2.3, 2009. microsoft research cambridge.Moole, B., & Valtorta, M. (2004). Sequential and parallel algorithms for causal explanation with background knowledge. International journal of uncertainty fuzziness and knowledge based systems, 12, 101–122.Murphy, K. (2007). Software packages for graphical models/bayesian networks. Bulletin International Society of Bayesian Analysis, 14.Neapolitan, R. (2004). Learning bayesian networks. Pearson Prentice Hall Upper Saddle River, NJ.Neyman, J. (1977). Frequentist probability and frequentist statistics. Synthese, 36(1), 97-131.Padmaja, T. M., Dhulipalla, N., Bapi, R. S., & Krishna, P. R. (2007). Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection. , 511–516. Available from http://dx.doi.org/10.1109/ADCOM.2007.131Padmanabhan, B., & Tuzhilin, A. (1998). A belief-driven method for discovering unexpected patterns. In Proceedings of the fourth international conference on knowledge discovery and data mining (Vol. 1, pp. 94–100).Padmanabhan, B., & Tuzhilin, A. (1999). Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3), 303–318.Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: discovering the minimal set of un-
  • 6 expected patterns. In Proceedings of the sixth acm sigkdd international conference on knowl- edge discovery and data mining (pp. 54–63). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/347090.347103Padmanabhan, B., & Tuzhilin, A. (2002). Knowledge refinement based on the discovery of unexpected patterns in data mining. Decision Support Systems, 33(3), 309-321.Pandit, S., Chau, D. H., Wang, S., & Faloutsos, C. (2007). Netprobe: a fast and scalable sys- tem for fraud detection in online auction networks. In Proceedings of the 16th international conference on world wide web (pp. 201–210). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/1242572.1242600Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann.Pearl, J. (2000). Causality: models, reasoning and inference. Cambridge University Press.Peng, Y., Wang, G., Kou, G., & Shi, Y. (2011, March). An empirical study of classification algorithm evaluation for financial risk prediction. Appl. Soft Comput., 11(2), 2906–2915. Available from http://dx.doi.org/10.1016/j.asoc.2010.11.028Phua, C., Alahakoon, D., & Lee, V. (2004). Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explorations Newsletter, 6(1), 50-59.Phua, C., Lee, V., Smith, K., & Gayler, R. (2005). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review, abs/1009.6119.Reddy, C., Chiang, H., & Rajaratnam, B. (2007). Trust-tech-based expectation maximization for learning finite mixture models. IEEE transactions on pattern analysis and machine intelligence, 1146-1157.Sarkar, S., & Murthy, I. (1996). Constructing efficient belief network structures with expert provided infor- mation. IEEE Transactions on Knowledge and Data Engineering, 8(1), 134-143.Schachter, R., & Heckerman, D. (1987). Thinking backward for knowledge acquisition. AI magazine, 8(3), 55.Schupbach, J. (2010). Comparing probabilistic measures of explanatory power.Shirazi, A. H. (2011). Reasoning with models of probabilistic knowledge over probabilistic knowledge. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.Silberschatz, A., & Tuzhilin, A. (1996, December). What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowl. and Data Eng., 8(6), 970–974. Available from http://dx.doi.org/10.1109/69.553165Srivastava, A., Kundu, A., Sural, S., & Majumdar, A. (2007). Credit card fraud detection using hidden markov model. IEEE Transactions on dependable and secure computing, 37-48.Sterritt, R., & Liu, W. (2001). Constructing bayesian belief networks for fault management in telecommu- nications systems. In 1st eunite workshop on computational intelligence in telecommunications and multimedia at eunite 2001 (pp. 149–154). EUropean Network on Intelligent TEchnologies for Smart Adaptive Systems.Suzuki, E. (2008). Pitfalls for categorizations of objective interestingness measures for rule discovery. Statistical Implicative Analysis, 383-395.Suzuki, E. (2009). Interestingness measures-limits, desiderata, and recent results. Proceedings of Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models, QIMIE.Tan, P., Kumar, V., & Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In Proceedings of the eighth acm sigkdd international conference on knowledge discovery and data mining (pp. 32–41).Tan, P., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4), 293-313.Tian, J., & Pearl, J. (2001). Causal discovery from changes. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 512–521).Ting, J., D’Souza, A., & Schaal, S. (2007). Automatic outlier detection: A bayesian approach. In Ieee international conference on robotics and automation, 2007 (pp. 2489–2494).
  • 7Van Allen, T., Singh, A., Greiner, R., & Hooper, P. (2008). Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artificial Intelligence, 172(4-5), 483–513.Van Koten, C., & Gray, A. (2006). An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology, 48(1), 59-67.Witten, I., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann Pub.Wooldridge, S. (2003). Bayesian belief networks. CISRO 2003.Youn, S., & McLeod, D. (2007). A comparative study for email classification. Advances and Innovations in Systems, Computing Sciences and Software Engineering, 387–391.Zhang, Y., Meratnia, N., & Havinga, P. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12(2), 159–170.